This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.

Context This dataset is of retail data collected from various retailers across the United States. The dataset is designed to mimic the complexity and challenges of real-world retail data, making it ideal for research and training purposes in the field of predictive analytics. The dataset covers a wide range of aspects, from customer demographics to product details, store information, and sales data.

Content The dataset contains 20,000 rows and 19 columns, each row representing a unique purchase made by a customer. The columns in the dataset are as follows:

CustomerID: A unique identifier for each customer. Age: The age of the customer. Gender: The gender of the customer. AnnualIncome: The annual income of the customer in USD. SpendingScore: A score (out of 100) that indicates the customer's spending behavior. ProductCategory: The category of the product that the customer bought. ProductPrice: The price of the product that the customer bought in USD. PurchaseDate: The date when the customer bought the product. StoreID: The ID of the store where the purchase was made. StoreLocation: The location of the store. PaymentMethod: The payment method used by the customer. DiscountApplied: Whether a discount was applied to the purchase (True or False). DiscountPercent: The percentage of discount applied to the purchase. ProductCost: The cost of the product to the retailer in USD. Profit: The profit made by the retailer on the sale in USD. FootTraffic: The number of people that visited the store on the day of the purchase. InventoryLevel: The inventory level of the product at the time of the purchase. MarketingExpenditure: The amount of money spent on marketing the product in USD. CompetitorPrice: The price of the same product at a competitor's store in USD.

Methodology

Through a relationship with NielsenIQ, the Kilts Center at the University of Chicago Booth School of Business provides multiple consumer datasets to academic researchers around the world.

Columbia has an agreement with the Kilts Center. Authorized faculty, graduate students, and research staff can apply to access this dataset.

Annual funding of the dataset is shared between the Program for Economic Research and the Libraries.

Funding for data analysis using the Columbia Data Platform is provided by the Program for Economic Research.

Usage

The Consumer Panel Data comprise a representative panel of households that continually provide information about their purchases in a longitudinal study in which panelists stay on as long as they continue to meet NielsenIQ's criteria. NielsenIQ consumer panelists use in-home scanners to record all of their purchases (from any outlet) intended for personal, in-home use. Consumers provide information about their households and what products they buy, as well as when and where they make purchases.

Years Available: Starting with 2004 and including annual updates.

Panel Size: 40,000–60,000 active panelists (varies by year), projectable to the total United States using household projection factors.

Panelists: Household demographic, geographic, and product ownership variables are included, as well as select demographics for the heads of household and other members.

• Demographic variables include household income range, size, composition, presence and age of children, marital status, type of residence, race, and Hispanic origin. Male and female heads of household also report age range, birth date, hours employed, education, and occupation. For other family members, birth date, employment, and relationship/sex are reported.
• Geographic variables include panelist zip code, FIPS state and county codes, region (East, Central, South, West), and Scantrack Market code (assigned by NielsenIQ, and called Syndicated Major Markets (SMM) starting in 2021).
• Product Ownership variables include kitchen appliances, TV items, and internet connection.

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Products: From 2004 to 2020, all 10 NielsenIQ food and nonfood Departments (~1.4 million UPC codes). These Departments are dry grocery, frozen foods, dairy, deli, packaged meat, fresh produce, nonfood grocery, alcohol, general merchandise, and health and beauty aids.

Starting in 2021 data, NielsenIQ implemented a new Product Hierarchy structure. The original Product Hierarchy of Departments, Product Groups, and Product Modules has been phased out. For now, Product Module Codes will still be available to make the transition smoother.

Product Characteristics: All products include UPC code and description, brand, multipack, and size, as well as NielsenIQ codes for Department, Product Group, and Product Module. Some products contain additional characteristics (e.g., flavor). Starting in 2021 data, NielsenIQ expanded the number of additional product characteristics.

**Purchases: **Each shopping trip contains the date, retail chain code, retail channel, first three digits of store zip code, and total amount spent. For each product purchased, the UPC code, quantity, price, and any deals/coupons are recorded. Note that retailer names are not available.

Geographies: Entire United States, divided into 62 major markets.

Retail Channels: All retail channels—grocery, drug, mass merchandise, superstores, club stores, convenience, health, and others.

Latest available data were used. If not stated otherwise. data are of 2007.1Gross Domestic Product in billion US-Dollar;2Gross domestic expenditure on R&D3Studies per billion US-Dollar spent on R&D42004;52005;62006;7R&D conducted by state and local governments is excluded;8Due to the lack of a comprehensive business register in South Africa. R&D expenditure may be underestimated by 10% to 15%.

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

A collection of over 75 charts and maps presenting key statistics on the farm sector, food spending and prices, food security, rural communities, the interaction of agriculture and natural resources, and more. How much do you know about food and agriculture? What about rural America or conservation? ERS has assembled more than 75 charts and maps covering key information about the farm and food sectors, including agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources. How much, for example, do agriculture and related industries contribute to U.S. gross domestic product? Which commodities are the leading agricultural exports? How much of the food dollar goes to farmers? How do job earnings in rural areas compare with metro areas? How much of the Nation’s water is used by agriculture? These are among the statistics covered in this collection of charts and maps—with accompanying text—divided into the nine section titles.

Retail Customer Segmentation

Uncovering Interesting Patterns in Purchase Behavior

By Abhishek Sharma [source]

About this dataset

This dataset contains customer purchase data from a retail store, offering insight into customers' shopping habits. Compiling transactions across multiple invoices, this dataset offers an opportunity to analyze and measure customer behavior in order to understand buying patterns and devise strategies to drive increased sales. From individual items purchased to total spend by country of origin, this comprehensive dataset allows for detailed segmentation analysis of how customers shop, where they spend their money and which products are most popular. With this information businesses can tailor their services more precisely and adjust their prices accordingly for maximum benefit. Dive in now and uncover valuable insights about your customers!

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How to use the dataset

This dataset is a great resource for customer segmentation analysis. Customer segmentation is the process of dividing customers into different subgroups based on characteristics such as age, gender, income, and purchasing behavior. By understanding these characteristics and customer behaviors, businesses can make more informed decisions on how to best target their marketing efforts to reach the right people with the right message.

Research Ideas

• Estimating customer lifetime value by taking into account the frequency of purchases, unit price and country of origin.
• Analyzing customer purchase patterns to identify which items are popular with different customers segments and tailor product/marketing strategies accordingly.
• Using machine learning algorithms to cluster customers based on their purchasing behavior and preferences to effectively target marketing efforts

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: customer_segmentation.csv | Column name | Description | |:----------------|:---------------------------------------------| | InvoiceNo | Unique identifier for each invoice. (String) | | StockCode | Unique identifier for each product. (String) | | Description | Description of the product. (String) | | Quantity | Number of items purchased. (Integer) | | InvoiceDate | Date of the invoice. (Date) | | UnitPrice | Price of each item. (Float) | | Country | Country of origin of the purchase. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Abhishek Sharma.

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

Follow data.kapsarc.org for timely data to advance energy economics research.

These data were generated as part of a two-and-a-half-year ESRC-funded research project examining the digitalisation of higher education (HE) and the educational technology (Edtech) industry in HE. Building on a theoretical lens of assetisation, it focused on forms of value in the sector, and governance challenges of digital data. It followed three groups of actors: UK universities, Edtech companies, and investors in Edtech. The researchers first sought to develop an overview of the Edtech industry in HE by building three databases on Edtech companies, investors in Edtech, and investment deals, using data downloaded from Crunchbase, a proprietary platform. Due to Crunchbase’s Terms of Service, only parts of one database are allowed to be submitted to this repository, i.e. a list of companies with the project’s classification. A report offering descriptive analysis of all three databases was produced and is submitted as well. A qualitative discursive analysis was conducted by analysing seven documents in depth. In the second phase, researchers conducted interviews with participants representing three groups of actors (n=43) and collected documents on their organisations. Moreover, a list of documents collected from Big Tech (Microsoft, Amazon, and Salesforce) were collected to contextualise the role of global digital infrastructure in HE. Due to commercial sensitivity, only lists of documents collected about investors and Big Tech are submitted to the repository. Researchers then conducted focus groups (n=6) with representatives of universities (n=19). The dataset includes transcripts of focus groups and outputs of writing by participants during the focus group. Finally, a public consultation was held via a survey, and 15 participants offered qualitative answers.

The higher education (HE) sector has been marketised for decades; but the speed, scope, and extent of marketisation has led key education scholars to conceptualise it as a global industry (Verger, Lubienski, & Steiner-Khamsi, 2016). Further, the use of technology to transform teaching and learning, as well as the profound digitalisation of universities more broadly, has led universities to collect and process an unprecedented amount of digital data. Education technology (EdTech) companies have become one of the key players in the HE industry and the UK has made EdTech one of its key pillars in its recent international education strategy (HM Government, 2019). EdTech companies are reporting unprecedented growth. In 2019, Coursera became a 'unicorn' (i.e. a company worth over $1 billion), while British-based FutureLearn secured £50 million investment by selling 50% shares of the company. Investment in EdTech is growing at an impressive rate and reached $16.3bn in 2018 (ET, 2019). While EdTech start-up companies strive to become 'unicorns' and profit from HE, so too might universities increasingly look for new ways of profiting from the wealth of digital data they produce.

The study of HE markets has so far focused on service-commodities. However, data and data products do not act like commodities. Commodities are consumed once used, but data is reproducible at almost zero marginal cost. New products and services can be created from data and monetised through subscription fees, an app, or a platform that does not transfer ownership, control, or reproduction rights to the user. Furthermore, data use creates yet more data, and the network effects increase the value of these platforms. Therefore, there is a new quality at play in the monetisation and marketisation of these digital HE products and services: 'assetization'. We are witnessing a widespread change from creating value via market exchange towards extracting value via the ownership and control of assets.

This research project aims to investigate these new processes of value creation and extraction in an HE sector that is digitalising its operations and introducing new digital solutions premised on the expansion of service fees. By introducing a focus on assets, and economic rents, this project offers a theoretically and empirically transformative approach to understand emerging HE markets and their implications for the HE sector. The assetization of HE is consequential because of the legal and technical implications for its regulation. It is also crucial to examine in any discussion about the legitimate and socially just arrangement and distribution of assets, their ownership, and their uses. The project employs an innovative, comparative, and participatory mixed-methods research design. It combines digital methods, interviews, observation, document analysis, deliberative focus groups, knowledge exchange and co-production with stakeholders, and public consultation. Data analysis will include quantitative and qualitative analysis of investment trends, comparative case studies of investors, EdTech companies and universities, and social network analysis.

The application of this research project is fourfold. First, it will help universities understand the emerging processes of assetization so they can develop policies and practices for protecting their rights. Second, it will assist entrepreneurs in finding ways to incorporate ethical and sustainable considerations in their innovation processes. Third, it will mediate between the financial interests of investors and the social function of universities. Here, it will provide evidence for policymakers on how to include assets in HE sector regulation. Finally, it will unpack potential forms of inequality that assetization might bring into the HE sector.

In the fourth quarter of 2024, TikTok generated around 186 million downloads from users worldwide. Initially launched in China first by ByteDance as Douyin, the short-video format was popularized by TikTok and took over the global social media environment in 2020. In the first quarter of 2020, TikTok downloads peaked at over 313.5 million worldwide, up by 62.3 percent compared to the first quarter of 2019.

              TikTok interactions: is there a magic formula for content success?

              In 2024, TikTok registered an engagement rate of approximately 4.64 percent on video content hosted on its platform. During the same examined year, the social video app recorded over 1,100 interactions on average. These interactions were primarily composed of likes, while only recording less than 20 comments per piece of content on average in 2024.
              The platform has been actively monitoring the issue of fake interactions, as it removed around 236 million fake likes during the first quarter of 2024. Though there is no secret formula to get the maximum of these metrics, recommended video length can possibly contribute to the success of content on TikTok.
              It was recommended that tiny TikTok accounts with up to 500 followers post videos that are around 2.6 minutes long as of the first quarter of 2024. While, the ideal video duration for huge TikTok accounts with over 50,000 followers was 7.28 minutes. The average length of TikTok videos posted by the creators in 2024 was around 43 seconds.

              What’s trending on TikTok Shop?

              Since its launch in September 2023, TikTok Shop has become one of the most popular online shopping platforms, offering consumers a wide variety of products. In 2023, TikTok shops featuring beauty and personal care items sold over 370 million products worldwide.
              TikTok shops featuring womenswear and underwear, as well as food and beverages, followed with 285 and 138 million products sold, respectively. Similarly, in the United States market, health and beauty products were the most-selling items,
              accounting for 85 percent of sales made via the TikTok Shop feature during the first month of its launch. In 2023, Indonesia was the market with the largest number of TikTok Shops, hosting over 20 percent of all TikTok Shops. Thailand and Vietnam followed with 18.29 and 17.54 percent of the total shops listed on the famous short video platform, respectively.

Demographical Shopping Purchases Data

Analyzing customer purchasing patterns and preferences

By Joseph Nowicki [source]

About this dataset

This dataset contains demographic information about customers who have made purchases in a store, including their name, IP address, region, age, items purchased, and total amount spent. Furthermore, this data can provide insights into customer shopping behaviour for the store in question - from their geographical information to the types of products they purchase. With detailed demographic data like this at hand it is possible to make strategic decisions regarding target customers as well as developing specific marketing campaigns or promotions tailored to meet their needs and interests. By gaining deeper understanding of customer habits through this dataset we unlock more possibilities for businesses seeking higher engagement levels with shoppers

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How to use the dataset

This dataset includes information such as customer's names, IP address, age, items purchased and amount spent. This data can be used to uncover patterns in spending behavior of shoppers from different areas or regions across demographics like age group or gender.

Research Ideas

• Analyze customer shopping trends based on age and region to maximize targetted advertising.
• Analyze the correlation between customer spending habits based on store versus online behavior.
• Use IP addresses to track geographical trends in items purchased from a particular online store to identify new markets for targeted expansion

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Demographic_Data_Orig.csv | Column name | Description | |:---------------|:------------------------------------------------------------------------------------------------| | full.name | The full name of the customer. (String) | | ip.address | The IP address of the customer. (String) | | region | The region of residence of the customer. (String) | | in.store | A boolean value indicating whether the customer made the purchase in-store or online. (Boolean) | | age | The age of the customer. (Integer) | | items | The number of items purchased by the customer. (Integer) | | amount | The total amount spent by the customer. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Joseph Nowicki.

Overview This dataset contains 50,000 fictional e-commerce transaction records, making it ideal for data analysis, visualization, and machine learning experiments. It includes user demographics, product categories, purchase amounts, payment methods, and transaction dates to help understand consumer behavior and sales trends.

Columns Transaction_ID – Unique identifier for each transaction User_Name – Randomly generated user name Age – Age of the user (18 to 70) Country – Country where the transaction took place (randomly chosen from 10 countries) Product_Category – Category of the purchased item (e.g., Electronics, Clothing, Books) Purchase_Amount – Total amount spent on the transaction (randomly generated between $5 and $1000) Payment_Method – Method used for payment (e.g., Credit Card, PayPal, UPI) Transaction_Date – Date of the purchase (randomly selected within the past two years)

Use Cases Sales and trend analysis – Identify which product categories are most popular Customer segmentation – Analyze spending behavior based on age and country Fraud detection – Detect unusual purchase patterns Machine learning projects – Train models for recommendation systems or revenue predictions

This dataset is synthetic and does not contain real user data. It can be used for research, experimentation, and educational purposes.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

U.S. Public Debt vs. GDP

Trends and Comparisons

By Charlie Hutcheson [source]

About this dataset

This dataset contains quarterly data on the US Gross Domestic Product (GDP) and Total Public Debt from 1947 through 2020. It provides a comprehensive view into the development of debt versus GDP over the years, offering insights into how our economy has grown and changed since The Great Depression. Explore this valuable information to answer questions such as: How do debt and GDP relate to one another? Has US government spending been outpacing wealth throughout history? From what sources does our national debt originate? This dataset can be utilized by economists, governments, researchers, investors, financial institutions, journalists — anyone looking to gain a better understanding of where our economy stands today compared to past decades

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How to use the dataset

This dataset, U.S. GDP vs Debt Over Time, contains quarterly data on the Gross Domestic Product (GDP) and Total Public Debt of the United States between 1947 to 2020. This can be useful for conducting research into how the total public debt relates to economic growth in the US.

The dataset includes 4 columns: Quarter , Gross Domestic Product ($mil), Total Public Debt ($mil). The Quarter column consists of strings that represent each quarter from 1947-2020 with a corresponding number (e.g., “Q1-1947”). The Gross Domestic Product ($mil) and Total Public Debt ($mil) columns consist of numbers that indicate the respective amounts in millions for each quarter during this same time period.

By analyzing this dataset you can explore various trends over different periods as it relates to public debt versus economic growth in America and make informed decisions about how certain policies may affect future outcomes. Additionally, you could also compare these two values with other variables such as unemployment rate or inflation rate to gain deeper insights into America’s economy over time

Research Ideas

• Comparing the quarterly growth in GDP with public debt to show the correlation between economic growth and government spending.
• Creating a bar or line visualization that compares the US’s total public debt to comparable economic powers like China, Japan, and Europe over time.
• Examining how changes in government deficit have contributed towards an increase in public debt by analyzing which quarters saw significant leaps of growth from one year to the next

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: US GDP vs Debt.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------------------------------------------| | Quarter | The quarter of the year in which the data was collected. (String) | | Gross Domestic Product ($mil) | The total value of all goods and services produced by the US in a given quarter. (Integer) | | Total Public Debt ($mil) | The total amount owed by the federal government. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Charlie Hutcheson.

The "Cloud-Enabled Marketing Strategy Dataset" is a collection of data designed to facilitate research on optimizing digital marketing strategies using cloud computing technologies. It encompasses a range of features that reflect consumer behavior, social media engagement, transaction patterns, market trends, and the performance of marketing campaigns. The dataset is ideal for developing machine learning models to predict customer responses, personalize marketing efforts, and assess the effectiveness of different marketing channels.

Key Features: Consumer Demographics:

customer_id: Unique identifier for each customer age: Age of the customer gender: Customer's gender income_level: Income category (Low, Medium, High) education_level: Education background Social Media Engagement:

platform: Social media platform (e.g., Facebook, Instagram, Twitter) likes_received: Number of likes received on posts comments_posted: Number of comments posted by the customer shares: Number of shares of content time_spent_per_week: Time spent on social media per week (in hours) Transaction Data:

average_order_value: Average value of customer orders frequency_of_purchases: Frequency of purchases made by the customer discounts_used: Whether the customer used discounts (binary: Yes/No) cart_abandonment_rate: Percentage of abandoned carts Market Trends:

search_trends_index: Popularity of a product or service over time seasonal_effect: Seasonality impact (e.g., Holiday, Summer) competitor_offers: Presence of competitor offers (binary: Yes/No) Customer Sentiment & Interaction:

sentiment_score: Sentiment of customer reviews or feedback (-1 to 1) customer_support_queries: Number of queries the customer has made response_time: Average time taken to resolve customer queries Marketing Campaign Performance:

campaign_id: Identifier for marketing campaigns marketing_channel: Type of marketing channel used (e.g., Email, TV Ads) ad_spent: Amount of money spent on the marketing campaign (USD) conversion_rate: Conversion rate from the campaign (percentage) ROI: Return on Investment for the campaign (percentage) Target Column:

customer_response: Whether the customer responded positively to a campaign (1 for positive, 0 for negative)

How much do fruits and vegetables cost? The USDA, Economic Research Service (ERS) estimated average prices for more than 150 commonly consumed fresh and processed fruits and vegetables. Reported estimates include each product's average retail price and price per edible cup equivalent (i.e., the unit of measurement for Federal recommendations for fruit and vegetable consumption). Average retail prices are reported per pound or per pint. For many fruits and vegetables, a 1-cup equivalent equals the weight of enough edible food to fill a measuring cup. USDA, ERS calculated average prices at retail stores using 2013, 2016, 2020, and 2022 retail scanner data from Circana (formerly Information Resources Inc. (IRI)). A selection of retail establishments—grocery stores, supermarkets, supercenters, convenience stores, drug stores, and liquor stores—across the United States provides Circana with weekly retail sales data (revenue and quantity).

USDA, ERS reports average prices per edible cup equivalent to inform policymakers and nutritionists about how much money it costs U.S. households to eat a sufficient quantity and variety of fruits and vegetables. Every 5 years the Departments of Agriculture and Health and Human Services release a new version of the Dietary Guidelines for Americans with information about how individuals can achieve a healthy diet. However, the average consumer falls short in meeting these recommendations. Many people consume too many calories from refined grains, solid fats, and added sugars, and do not eat enough whole grains, fruits, and vegetables. Are food prices a barrier to eating a healthy diet? USDA, ERS research using this dataset examines the quantity and variety of fruits and vegetables that a household can afford with a limited budget. See:

• The Cost of Satisfying Fruit and Vegetable Recommendations in the Dietary Guidelines
• For Supplemental Nutrition Assistance Program (SNAP) Households, Fruit and Vegetable Affordability Is Partly a Question of Budgeting
• Americans Still Can Meet Fruit and Vegetable Dietary Guidelines for $2.10-$2.60 per Day

USDA, ERS fruit and vegetable prices will be updated each year, subject to data availability. When generating estimates using 2013, 2016, 2020, and 2022 data, USDA, ERS researchers priced similar fruit and vegetable products. However, because of different methods for coding the underlying Circana data, the entry of new products into the market, the exit of old products from the market, and other factors, the data are not suitable for making year-to-year comparisons. These data should not be used for making inferences about price changes over time.

For data on retail food price trends, see the USDA, ERS’ Food Price Outlook (FPO). The FPO provides food price data and forecasts changes in the Consumer Price Index (CPI) and Producer Price Index (PPI) for food.

For additional data on food costs, see the USDA, ERS’ Purchase to Plate (PP-Suite). The PP-Suite reports a U.S. household’s costs to consume other categories of foods in addition to fruits and vegetables, such as meats, seafood, and cereal and bakery products. Food groupings in the PP-Suite are based on the USDA, Agricultural Research Service’s (ARS) Food and Nutrient Database for Dietary Studies (FNDDS). This allows users to import price estimates for foods found in USDA dietary survey data. USDA, ARS’ FNDDS food groupings are broader than the specific food products priced for constructing this data product. They also include both conventional and organic products. For example, the PP-Suite average price to consume broccoli purchased raw is the average price paid for organic and conventional heads, crowns, and florets. By contrast, this data product distinguishes and separately reports the average costs to consume conventional raw broccoli purchased as heads and florets.

• Fruit
• Vegetables

Nintendo Switch Dedicated Video Game Sales Units: 2017-2020

Quantifying Unit Sales Performance

By Charlie Hutcheson [source]

About this dataset

This dataset contains dedicated video game sales unit data for the Nintendo Switch platform from 2017 to 2020, as reported by official Nintendo Investor Relations. It provides an snapshot of consumers’ buying trends over the past four years and helps us gain insightful understanding into the introduction, expansion and success of this platform across global markets. The data can be used to analyze multiple aspects such as performance of specific titles/genres/franchises, changes in market expectations over time and more. This chart helps to visualize the dynamic changes in these sales units over that four-year timeframe. From this chart we can gain valuable understanding about how successful various releases have been on this gaming console, what titles drove its popularity levels and more useful insights that could help other developers in creating future products for similar platforms

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How to use the dataset

This dataset contains historical sales unit data for the Nintendo Switch platform from 2017 to 2020. The original visualization provides a clear visual representation of the number of sales units over time. It is easy to discern which months have seen higher levels of sales, and which have seen lower ones.

Using this dataset, users can perform various analyses on the results to gain further insights into consumer trends and buy behavior associated with the Nintendo Switch platform. Users can also glean information on pricing strategies taken by Nintendo as well as consumer preferences over time in order to inform future business decisions.

In order to maximize use of this data set, users are encouraged to consider questions such as: What types of games do consumers prefer? How has their taste changed over time? What is the average amount spent per game by region or country? How often are certain consoles purchased or rented? And what role do discounts or promotions play in influencing purchasing decisions? By exploring these questions, users can begin understanding how different factors may be affecting overall demand for a product associated with the Nintendo Switch platform.

By analyzing this dataset, users also get an insight into how other competitors within the industry are affecting sales performance and allowing them take steps necessary for either surpassing competitors or maintaining dominance through suitable tactics like improved marketing campaigns or better-priced products that appeal more strongly customers’ needs and wants . In addition, examining this data enables companies keenly understand customer demands at detailed levels including whether customers prefer switch game bundles with extra features like custom skins etc., titles released during special times such as a holiday season that incite strong demand among buyers and also relevant discounts/promotions offered during times when people want/needing much needed break from regular routine life.. Ultimately , gaining greater insight into customer objectives allows firms efficiently manage their costs while maximizing profits through effective decisions based on reliable datasets such as that contained in this one instead rarely updated manual counts/observations which dont just lack comprehensiveness but also accuracy in nature.

Research Ideas

• Producing a mobile application to present the sales units in an intuitive and interactive way;
• Utilizing the data for machine learning algorithms to predict and analyze trends in Nintendo Switch dedicated video game sales units;
• Creating infographics and visualizations that can be used for promotional materials or to educate customers about the success of Nintendo Switch dedicated game sales

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the or...

📹Project Video available on YouTube - https://youtu.be/WmJYHz_qn5s

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

Realistic Synthetic - AI Financial & Market Data for Gemini(Google), ChatGPT(OpenAI), Llama(Meta)

This dataset provides a synthetic, daily record of financial market activities related to companies involved in Artificial Intelligence (AI). There are key financial metrics and events that could influence a company's stock performance like launch of Llama by Meta, launch of GPT by OpenAI, launch of Gemini by Google etc. Here, we have the data about how much amount the companies are spending on R & D of their AI's Products & Services, and how much revenue these companies are generating. The data is from January 1, 2015, to December 31, 2024, and includes information for various companies : OpenAI, Google and Meta.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Finance or Share Market domain.

From this dataset, we extract various insights using Python in our Project.

1) How much amount the companies spent on R & D ?

2) Revenue Earned by the companies

3) Date-wise Impact on the Stock

4) Events when Maximum Stock Impact was observed

5) AI Revenue Growth of the companies

6) Correlation between the columns

7) Expenditure vs Revenue year-by-year

8) Event Impact Analysis

9) Change in the index wrt Year & Company

These are the main Features/Columns available in the dataset :

1) Date: This column indicates the specific calendar day for which the financial and AI-related data is recorded. It allows for time-series analysis of the trends and impacts.

2) Company: This column specifies the name of the company to which the data in that particular row belongs. Examples include "OpenAI" and "Meta".

3) R&D_Spending_USD_Mn: This column represents the Research and Development (R&D) spending of the company, measured in Millions of USD. It serves as an indicator of a company's investment in innovation and future growth, particularly in the AI sector.

4) AI_Revenue_USD_Mn: This column denotes the revenue generated specifically from AI-related products or services, also measured in Millions of USD. This metric highlights the direct financial success derived from AI initiatives.

5) AI_Revenue_Growth_%: This column shows the percentage growth of AI-related revenue for the company on a daily basis. It indicates the pace at which a company's AI business is expanding or contracting.

6) Event: This column captures any significant events or announcements made by the company that could potentially influence its financial performance or market perception. Examples include "Cloud AI launch," "AI partnership deal," "AI ethics policy update," and "AI speech recognition release." These events are crucial for understanding sudden shifts in stock impact.

7) Stock_Impact_%: This column quantifies the percentage change in the company's stock price on a given day, likely in response to the recorded financial metrics or events. It serves as a direct measure of market reaction.

*This dataset is a tool for analyzing and managing greenhouse gas (GHG) emissions along the supply chain for various U.S. industries and commodities, built around the 2017 North American Industry Classification System (NAICS), which categorizes economic activities.*

What It's About:

*The dataset provides metrics on how much greenhouse gas is emitted per dollar spent on specific goods or services, including emissions from:*

Direct production

(e.g., making a product in a factory)

Supply chain margins

(e.g., emissions from transportation, distribution, and retail activities)

*By combining these two, the dataset offers a full picture of the carbon footprint of a product from production to consumption.*

Why It Matters:

Policy and Regulation:

Helps governments and organizations set better policies for reducing emissions across industries.

Corporate Responsibility:

Businesses can identify high-emission areas in their supply chains and work on reducing their environmental impact.

Consumer Awareness:

Shows how spending on certain goods or services contributes to global emissions.

How It's Structured:

Each row corresponds to a specific commodity (e.g., soybean farming, wheat farming, etc.), and the columns provide:

*1. The NAICS category it belongs to.*

*2. The emission factors in kilograms of CO2 equivalent (CO2e) per U.S. dollar, broken down into:*

*Direct emissions from production.*

*Indirect emissions from margins (like distribution).*

*Combined total emissions.*

*This is an important resource for understanding the environmental impact of supply chains across industries.*

Attribution:

Publisher: U.S. EPA Office of Research and Development (ORD)

https://www.epa.gov/

LICENCE: The U.S. Environmental Protection Agency (EPA) typically releases its data in the public domain unless stated otherwise. The EPA does not guarantee the data's accuracy or suitability for specific purposes and disclaims liability for any improper or incorrect use. Datasets from external sources are not under EPA's control, and any commercial product references do not imply endorsement.

https://pasteur.epa.gov/license/sciencehub-license.html

https://www.usa.gov/government-copyright

NO ENDORSEMENT: The U.S. EPA does not endorse this work or its conclusions.

DISCLAIMER: Please independently verify the data and it's derivation(s) before applying it to research or decision-making.